Database systems and search and retrieval from such databases are known. For example, U.S. Pat. No. 5,911,139 to Jain et al. describes a visual image database search engine which allows for different schema. A schema is a specific collection of primitives to be processed and a corresponding feature vector is used for similarity scoring. In particular, a system and method for content-based search and retrieval of visual objects computes a distance between two feature vectors in a comparison process to generate a similarity score.
U.S. Pat. No. 6,778,995 to Gallivan describes a system and method for efficiently generating cluster groupings in a multi-dimensional concept space. A plurality of terms is extracted from documents of a collection of stored, unstructured documents. A concept space is built over the collection and terms correlated between documents such that a vector may be mapped for each correlated term. Referring to FIG. 14 of the '995 patent, a cluster is populated with documents having vector differences falling within a predetermined variance such that a view may be generated of overlapping clusters.
U.S. Pat. No. 7,127,372 to Boysworth describes an improved regression-based qualitative analysis algorithm when a mixture, not in a library of spectra, and being an “unknown” is subjected to regression analysis of “peaks” in a residual error computed between an estimated spectrum and a measured spectrum. The process is repeated using information from a retro-regression.
U.S. Pat. No. 7,236,971 to Shatdal et al. describes a method and system for deriving data through interpolation in a database system. A parallel database system has plural processing units capable of performing interpolation of data in parallel.
U.S. Pat. No. 7,318,053 to Cha et al. describes an indexing system and method for nearest neighbor searches in high dimensional databases using vectors representing objects in n-dimensional space and local polar coordinates for vectors such that a query data vector is requested to find “k” nearest neighbors to the query vector of the vectors in the n-dimensional space.
Haining, “Exploratory Spatial Data Analysis in a Geographic Information System,” The Statistician 47, Part 3, 457-469 (1998) describes a software system involving the exploratory spatial data analysis features of the ARC/INFO geographical information system and identifies spatial patterns of geographic locations.
With respect to crime analysis in the twentieth century, two of the most important scientific developments include fingerprint and DNA analysis. Fingerprint analysis has resulted, for example, in the Automatic Fingerprint Identification System or AFIS, and DNA analysis has resulted, for example, at the federal level, in the development of the CODIS database of DNA profiles which are relevant to crime scene investigation and offender profiling and matching. Over the years, fuzzy logic, artificial intelligence, neural networks, data mining, linkage analysis, geographic and time of incident graphical analysis and even Bayesian networks have been used to identify patterns in different types of crimes (arson, burglary, murder, sexual assault among others) and to study these crime types by performing linkage analysis among, modus operandi or MO, crime signatures (e.g., always taking a trophy), ritual, and intentional deception by the offender. There has been a dialog in the literature between those advocating a Hercule Poirot human inference model versus a more scientific-based model for criminal profiling, especially profiling serial offenders (individuals or groups of individuals). David J. Icove et al., in his article “Application of Pattern Recognition in Arson Investigation.” Fire Technology, pp. 35-41, 1975 highlights a study conducted with the Prince George's County Fire Department, in which there is discussed the application of pattern recognition methods to the development of techniques for aiding an arson investigator in determining trends in incendiary and suspicious fires. Dr. Trove further developed an algorithm for crime pattern recognition at the University of Tennessee and published “Principles of incendiary Crime Analysis,” June. 1979, in which Dr. Icove discusses a model called an arson pattern recognition system (ARPS) for arson-fraud crimes which comprises 1) detection 2) prediction and 3) prevention. The model involves the investigation of an intentional incendiary event and adding object data to a database to continuously improve prevention and prediction of individual arsonists and groups of arsonists. Further work by Dr. Icove is discussed in his publications: “Automated Crime Profiling,” FBI Law Enforcement Bulletin, pp. 27-30, 1986 and his “Motive-Based Offender Profiles of Arson and Fire-Related Crimes,” FBI Law Enforcement Bulletin, pp. 17-23, 1987. In particular, the 1986 automated crime profiling article suggests a system depicted in FIG. 22 (PRIOR ART). 1) Violent crime incidents are reported and via 2) media reports, 3) crime scene processing and 4) VICAP crime reports and 5) violent crime research data are used to build 6) an artificial intelligence system. The artificial intelligence system outputs 7) crime scene profiles and 8) strategic and training implications which are then input to 1) violent crime incident investigation. Trove claims that 1) useless investigative paths may be eliminated, 2) similar profiles, case studies and the like may be preserved and recalled, 3) complex criminal network problems may be displayed. 4) decision rules accelerate processing time, 5) the expert system will output useful information based on the knowledge base, and 6) complex criminal problems may be solved, The arson study included data such as day of week, time of day, full/new moon and so arson crimes of juvenile offenders were identified. Also, in 1986, “Criminal Profiling from Crime Scene Analysis,” Douglas et al., Behavioral Sciences and the Law, vol. 4, no. 4, pp. 401-421, suggests in FIG. 1 thereof a criminal profiling process which involves “Decision Process Models” as element 2 of a complex feedback process similar to that of FIG. 22 (PRIOR ART) of Icove, but an emphasis is placed more on the human criminal profiler than on the use of an intelligent pattern recognition modeling. Anne Davies in her article. “Rapists' Behavior: A Three Aspect Model as a Basis for Analysis and the Identification of Serial Crime,” Forensic Science International, 55, 1992. pp. 173-194, suggests a behavioral model broken into three “aspects” which are modus operandi, sexual and personal gratification and attitude and intimacy. These, in turn, are broken down into twenty-three attributes ranging from choice of time and location under modus operandi to use of “compliments” under attitude and intimacy. Most recently, referring to FIG. 23 (PRIOR ART), Craig Bennell et al., in “the Impact of Data Degradation and Sample Size on the Performance of Two Similarity Coefficients used in Behavioral Linkage Analysis,” Forensic Science International, 199, 2010, pp. 85-92, (received 17 Jun. 2009 and prior art to the extent of the 17 Jun. 2009 version or public use earlier than 25 Jun. 2009) shows a more detailed behavioral hierarchy at four levels and comprising thirty-six attributes at its deepest level from a blitz attack under control behaviors to steals personal items under theft behaviors, the behaviors further including escape behaviors and sexual behaviors. The Federal Bureau of Investigation (FBI) later published: “A Report of Essential Findings from a Study of Serial Arsonists,” Allen D. Sapp et al. (including Dr. Icove), 1994 (113 pages). In this detailed study of criminal arson activity patterns, findings include attributes of serial arsonists such as age, gender, race, physical attributes, marital status, marital history, education, occupation, military record, intelligence quotient and sexual preference. Moreover, the life history of subjects resulted in findings such as criminal history, institutional history, medical history, psychological history including suicide attempts, work history, social and family histories and the like. Motives were characterized as vandalism, revenge, excitement, crime concealment, profit and so on. Numerous tables provide details such as the quantitative result that, of 39 cases studied of serial arsonists, 28.2% of these arsonists were classified as “menial laborer” and 23.1% were classified as “skilled laborer.” U.S. Pat. No. 5,781,704 issued Jul. 14, 1998, to Rossmo, describes “An Expert System Method of Performing Crime Site Analysis.” Rossmo suggests utilizing the Manhattan distance as one means of ascertaining a probable location of a criminal and showing probability of the criminal being at the location.
Other systems and database technologies are known which incorporate multivariate statistical analysis and, in particular, principal component analysis, from patent and non-patent literature and other technologies which utilize a geographic information system (GIS).